System and method for low density spreading modulation detection

ABSTRACT

In one embodiment, a method for blindly detecting low density activity includes receiving, by a first node from a second node, a signal and executing a joint message passing algorithm (JMPA) on the signal, where executing the JMPA includes jointly producing a decoded signal and an activity list in accordance with the decoded signal, and calculating a plurality of a priori probabilities in accordance with a plurality of log likelihood ratios (LLRs) corresponding to the signal and a plurality of decoded LLRs.

This application is a continuation of U.S. patent application Ser. No.13/917,319, filed on Jun. 13, 2013, entitled “System and Method for LowDensity Spreading Modulation Detection,” which claims the benefit ofU.S. Provisional Application Ser. No. 61/737,601 filed on Dec. 14, 2012,and entitled “System and Method for Low Density Spreading ModulationDetection,” which application is hereby incorporated herein byreference.

TECHNICAL FIELD

The present invention relates to a system and method for wirelesscommunications, and, in particular, to a system and method for lowdensity spreading (LDS) modulation detection.

BACKGROUND

Code division multiple access (CDMA) is a channel access method used bycommunication technologies, where several users send informationsimultaneously over a single channel, e.g. common frequencies, usingdifferent spreading signatures. CDMA involves spread-spectrumtechnology, where the modulated coded signal has a much higher databandwidth than the data being communicated. Low density spreading (LDS)is a CDMA technique that utilizes sparse spreading signatures. LDS maytypically require the receiver to have knowledge of the spreadingsignatures used by the various transmitters, which may significantlyincrease overhead. Hence, techniques for reducing overhead during LDScommunications are desired.

SUMMARY

An embodiment method for blindly detecting low density activity includesreceiving, by a first node from a second node, a signal and executing ajoint message passing algorithm (JMPA) on the signal, where executingthe JMPA includes jointly producing a decoded signal and an activitylist in accordance with the decoded signal, and calculating a pluralityof a priori probabilities in accordance with a plurality of loglikelihood ratios (LLRs) corresponding to the signal and a plurality ofdecoded LLRs.

An embodiment first node includes a processor and a non-transitorycomputer readable storage medium storing programming for execution bythe processor. The programming includes instructions to receive a signalfrom a second node, and execute a joint message passing algorithm (JMPA)on the signal, to jointly produce an activity list in accordance with adecoded signal, and calculate a plurality of a priori probabilities inaccordance with a plurality of log likelihood ratios (LLRs)corresponding to the signal and a plurality of decoded LLRs.

An embodiment non-transitory computer readable storage medium storesprogramming for execution by a processor. The programming includesinstructions to receive a signal from a second node and execute a jointmessage passing algorithm (JMPA) on the signal, to jointly produce anactivity list in accordance with a decoded signal, and calculate aplurality of a priori probabilities in accordance with a plurality oflog likelihood ratios (LLRs) corresponding to the signal and a pluralityof decoded LLRs.

The foregoing has outlined rather broadly the features of an embodimentof the present invention in order that the detailed description of theinvention that follows may be better understood. Additional features andadvantages of embodiments of the invention will be describedhereinafter, which form the subject of the claims of the invention. Itshould be appreciated by those skilled in the art that the conceptionand specific embodiments disclosed may be readily utilized as a basisfor modifying or designing other structures or processes for carryingout the same purposes of the present invention. It should also berealized by those skilled in the art that such equivalent constructionsdo not depart from the spirit and scope of the invention as set forth inthe appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawing, in which:

FIG. 1 illustrates an embodiment system for low density spreading (LDS)modulation detection;

FIG. 2 illustrates an embodiment method for LDS modulation detection;

FIG. 3 illustrates a factor graph representation of a spreading matrix;

FIG. 4 illustrates another embodiment method for LDS modulationdetection;

FIG. 5 illustrates a graph of block error rate (BLER) versus signal tonoise ratio (SNR) for six active user equipments (UEs) and no poweroffset;

FIG. 6 illustrates a graph of BLER versus SNR for six active UEs and a 1decibel (dB) power offset;

FIG. 7 illustrates a graph of BLER versus SNR for four active UEs and nopower offset;

FIG. 8 illustrates a graph of BLER versus SNR for four active UEs and a1 dB power offset;

FIG. 9 illustrates another embodiment method for LDS modulationdetection;

FIG. 10 illustrates a factor graph representation of a spreading matrixwith inactive signatures;

FIG. 11 illustrates constellation points for a full factor graphconsidering active and inactive signatures;

FIG. 12 illustrates an additional embodiment method for LDS modulationdetection; and

FIG. 13 illustrates a block diagram of an embodiment of ageneral-purpose computer system.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. The figures aredrawn to clearly illustrate the relevant aspects of the embodiments andare not necessarily drawn to scale.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

It should be understood at the outset that although an illustrativeimplementation of one or more embodiments are provided below, thedisclosed systems and/or methods may be implemented using any number oftechniques, whether currently known or in existence. The disclosureshould in no way be limited to the illustrative implementations,drawings, and techniques illustrated below, including the exemplarydesigns and implementations illustrated and described herein, but may bemodified within the scope of the appended claims along with their fullscope of equivalents.

Aspects of this disclosure reduce overhead in low density spreading(LDS) networks by discovering (or otherwise identifying) LDS signaturesvia blind detection, thereby circumventing the need to communicateactive signature assignments over the control channel. Hence,embodiments of this disclosure allow a receiver to perform LDS detectionwithout having prior knowledge of the active signatures used to performthe transmissions. In one example, blind detection is achieved using adecorrelator. A received signal is decorrelated to produce an activesignature list. Then, data is decoded from the received signal using theactive signature list. In another example, blind detection is achievedusing joint signature and data detection using MPA (JMPA). The data isdecoded from the received signal while the active signature list isdetermined.

FIG. 1 illustrates system 100, a wireless communications network thatmay be used for LDS modulation detection. System 100 includes transmitpoint 102, which provides voice and/or data wireless communicationservices to user equipments (UEs), such as UE 104, UE 106, and UE 108.Three UEs are pictured, but more or fewer UEs may be coupled to transmitpoint 102. Transmit point 102 may also be referred to as an access node,access point, or Node-B. Transmit point 102 transmits downlinkinformation to UEs 104, 106, and 108, and receives uplink informationfrom UEs 104, 106, and 108.

In an example using code division multiple access (CDMA), knowledge ofactive signatures is required at transmit point 102 for decoding signalsreceived from UEs 104, 106, and 108, which requires signaling overhead.FIG. 2 illustrates flowchart 110 for a method of LDS modulationdetection with knowledge of active signatures. UE 104, in step 116,examines a signature pool, which contains the pool of signatures thatmay be used. UE 104 has prior knowledge of the signature pool, becausethe signature pool is the common knowledge in the network. Because thesignature pool is fixed, it can be set in advance.

Then, in step 114, transmit point 102 receives an active signature listfrom UE 104. UE 104 signals the active signature list to transmit point102. In one example, the active signature list is explicitly signaledthrough the control channel. In another example, the active signaturelist is explicitly signaled through higher layer signaling. The activesignature list is a subset of the signature pool.

Finally, in step 112, UE 104 performs LDS modulation detection on areceived signal from transmit point 102 using the active signature list.LDS modulation involves using a message passing algorithm (MPA) based onbelief propagation (BP). Message passing algorithm (MPA) is a multi-userdetection based on belief propagation (BP) that may be used for LDSmodulation detection. MPA takes advantage of the sparsity of signaturesto reduce the complexity of multi-user detection. Also, BP is atechnique used for performing inference on graphical models, such asBayesian networks and Markov random fields. A spreading matrix may beused in step 112, where the number of rows indicates the spreadingfactor and the columns represent the active signatures (UEs). An examplespreading matrix with a spreading factor of four and up to 6 activesignatures (UEs) is given by:

$S = {\begin{bmatrix}0 & 1 & {- 1} & 0 & i & 0 \\1 & 0 & i & 0 & 0 & {- 1} \\0 & {- 1} & 0 & i & 0 & 1 \\i & 0 & 0 & {- 1} & 1 & 0\end{bmatrix}.}$

The nonzero values of the spreading matrix are elements. The values inspreading matrix S are either 0 or have a normalization of 1. Many zerosin the spreading matrix indicate a low density structure of the matrix,which helps in the decoding be performed with a low complexity.

FIG. 3 illustrates factor graph 130, the factor graph representation ofspreading matrix S. Factor graph 130 includes variable nodes 132 whichare linked to function nodes 134. Variable nodes 132 correspond to theUEs (signatures), while function nodes 134 correspond to the receivedsignals. The four function nodes are for four signals received over fourtones, with a spreading factor of four. In quadrature phase-shift keying(QPSK), each branch contains four probabilities corresponding to eachconstellation point. The connections between variable nodes 132 andfunction nodes 134 correspond to the nonzero values of spreading matrixS. In an example, factor graph 130 is used to perform MPA iteratively.Initially, a vector containing a priori probabilities is used forvariable nodes 132. These a priori values are used with the spreadingmatrix, S, to calculate the values at function nodes 134. The values atvirtual nodes 132 are then calculated based on the values at functionnodes 134. The values at function nodes 134 and variable nodes 132 areiteratively calculated. This back and forth information passing isrepeated until the values at virtual nodes 132 converge on a solution.The converged probability values at variable nodes 132 are thenprocessed to determine the six values for the six UEs. This updating thevectors or values back and forth between the variable nodes and thefunction nodes is also referred to as message passing or exchangebetween the two node sets.

In another example, LDS modulation may be blindly detected. Blindlydetecting LDS modulation is performed without knowledge of the activesignatures, which reduces the signaling overhead. FIG. 4 illustratesflowchart 120 showing a method of blindly detecting LDS modulation usinga decorrelator. Initially, in step 126, the signature pool is examined.

Then, in step 124, signature decorrelation is performed based on thesignature pool and the received signal. The signature decorrelatorproduces a hard or soft list of active signatures, which is a subset ofthe signature pool.

A complementary signature matrix (CSM) is determined based on thespreading matrix. In the complimentary signature matrix, the zeros arein the same positions as in the signature matrix. The elements of thecomplimentary signature matrix are assigned to the same value or thenegative of the value in the same position in the correspondingspreading matrix. The elements of the spreading matrix have a value ofzero or a constant normalization value. For example, the elements ofspreading matrix S have a normalization value of 1. Each signature ofthe spreading matrix has at most one intersection, or common non-zeroposition with all other signatures. Spreading matrix S satisfies theseconditions. Each column of the complimentary signature matrix isorthogonal to the corresponding column of the signature matrix. Thecomplimentary signature matrix of S is given by:

$\overset{\sim}{S} = {\begin{bmatrix}0 & {- 1} & 1 & 0 & {- i} & 0 \\{- 1} & 0 & i & 0 & 0 & 1 \\0 & {- 1} & 0 & {- i} & 0 & 1 \\i & 0 & 0 & {- 1} & 1 & 0\end{bmatrix}.}$

Assuming a spreading factor of N, the receive signal corresponding tothe spread transmission signal is:

${Y = {\begin{pmatrix}y_{1} \\\vdots \\y_{N}\end{pmatrix} = {{\sum\limits_{k = 1}^{J}{I_{k}H_{k}S_{k}u_{k}}} + z}}},$where I_(k) is the indicator parameter indicating whether the kthsignature is active, H_(k) is the channel matrix, S_(k) is the kthsignature, u_(k) is the transmitted data for user k, and z is additivewhite Gaussian noise. H_(k), may be written as:H _(k)=diag(h _(k1) , . . . ,h _(kN)).For a downlink scenario, H_(k) is the same for all channels. S_(k) isthe kth column of the spreading matrix. I_(k) has a value of either 0 or1.

The received signal is decorrelated by two signatures, S_(i) and {tildeover (S)}_(i). For example,

${\Psi_{i} = {{S_{i}^{H}Y} = {{\sum\limits_{k = 1}^{J}{I_{k}{H_{k}\left( {S_{i}^{H}S_{k}} \right)}u_{k}}} + n_{i}}}},{and}$${\overset{\sim}{\Psi}}_{i} = {{{\overset{\sim}{S}}_{i}^{H}Y} = {{\sum\limits_{k = 1}^{J}{I_{k}{H_{k}\left( {{\overset{\sim}{S}}_{i}^{H}S_{k}} \right)}u_{k}}} + {{\overset{\sim}{n}}_{i}.}}}$The output of the decorrelator is then given by:Γ_(i)=|Ψ_(i)|²−|{tilde over (Ψ)}_(i)|² ,i=1, . . . ,J, andwhere (.) denotes the average over all LDS blocks in the bandwidth ofinterest. The decision on the active signatures is based on:{right arrow over (Γ)}=(Γ₁, . . . ,Γ_(J)).

In some scenarios, assumptions may be made that simplify the design ofthe decorrelator. There may be channel state information (CSI). Whenthere channel knowledge of the propagation channel, for example withzero forcing, the decorrelated signal is given by:Ψ_(i) =S _(i) ^(H) H _(i) ⁻¹ Y,where H_(i) ⁻¹ is the inverse of the channel. When there ismaximal-ratio combining (MRC), the decorrelated signal is given by:Ψ_(i) =S _(i) ^(H) H _(i) ^(H) Y.However, without channel knowledge, the decorrelated signal is given by:Ψ_(i) =S _(i) ^(H) Y.

The signature detection used may be hard detection or soft detection. Inhard detection, the decision of whether the signature is used is basedon a function. However, in soft detection, the probability that thesignature is active is determined. When hard detection is used, whetherthe signature is active or not is given by:I _(i) =f({right arrow over (Γ)}),i=1, . . . ,J.When soft detection is used, the probability that a signature is activeis given by:P(I _(i)=1)=f({right arrow over (Γ)}),i=1, . . . ,J.

The ordering of the signatures may be known or unknown. For example, theordering may be hierarchical, where all inactive signatures are afterall active signatures. When a signature k is active, all signatures withlower indices are also active, and when signature k is not active, allsignatures with higher indices are also inactive. With hierarchicalordering, the probability that a signature is active is given by:P{I _(k)=1|I _(l)=0}=0, for k<l.

Alternatively, all active signatures may be after all inactivesignatures. When the ordering is unknown, all I_(k)s are independent ofeach other.

After decorrelation, LDS detection is performed in step 122 based on thereceived signal and the active signature list produced by decorrelation.The LDS data detection uses MPA based on BP to produce the decoded data.A spreading matrix, such as S, and a factor graph representation, suchas factor graph representation 130 may be used to decode the data. Theknown active signatures indicate the factor graph. Information isiteratively passed back and forth between variable nodes 132 andfunction nodes 134. The LDS data detector finally produces decoded data.

When iterative soft signature detection is performed, signaturedecorrelation is again performed after LDS data detection, in step 124,based on the decoded data and the signature pool. Then, LDS datadetection is again performed based on the received signal and theupdated soft active signature list.

Some assumptions may be used to simplify the decorrelator design for adownlink scenario. Because the total transmission power is constrainedin a downlink scenario, the power of each active signature will bescaled by the total number of active signatures when there is no poweroffset between signatures. For example, the power of the ith signatureis given by:

${P_{i} = \frac{P_{tot}}{N_{A}}},$where P_(tot) is the total power and N_(A) is the number of activeusers.

Also, because the transmitter knows the number of active signatures in adownlink scenario, it may use them in a pre-determined order which isknown to the UEs. For example, the transmitter may transmit thepre-determined order to the UEs. Additionally, the knowledge of thechannel may be used for signature detection in a downlink scenario.Thus, blind signature detection with decorrelation may be performed withknown CSI, hard detection, and a known signature ordering.

LDS may be used over M tones, where n=M/N blocks of LDS coders. Thereceiver computes the parameters |Ψ_(i)(j)|² and |{tilde over(Ψ)}_(i)(j)|² for the jth code block, where i=1, . . . , J. Thedecorrelator output is computed as:

$\Gamma_{i} = {\frac{1}{n}{\sum\limits_{j = 1}^{n}{\left( {{{\Psi_{i}(j)}}^{2} - {{{\overset{\sim}{\Psi}}_{i}(j)}}^{2}} \right).}}}$

Then, the decorrelation output is normalized using:{right arrow over (Γ)}=(Γ₁, . . . ,Γ_(J)),such that

${\sum\limits_{k = 1}^{J}\Gamma_{k}} = 1.$χ_(k) is defined as:χ_(k)=Σ_(i=1) ^(k)Γ_(i) −kΓ _(k+1).The following algorithm may be used for some pre-determined thresholdvalues th_(k):

Set k = 1; While N_(A) not found and k ≦ J do the following:   If X_(k)≧ th_(k)     N_(A) = k;     Return;   End   k = k + 1; End If N_(A) isnot determined, N_(A) = J;N_(A) is the number of active signatures which, in the case ofpre-determined signature order, provides the active signatures.

FIG. 5 illustrates a graph of block error rate (BLER) versus signal tonoise ratio (SNR) in decibels (dB) for the downlink scenario for LDSmodulation with six active UEs and no power offset. Curve 202 is withperfect Channel State Information (CSI) and perfect signature knowledge,curve 204 is for perfect CSI with signature decorrelation using an MPA,curve 206 is for channel estimation with perfect signature knowledge,and curve 208 is for channel estimation with signature decorrelationusing an MPA. Thus, using signature decorrelation with an MPA for sixactive UEs and no power offset performs similarly to LDS detection withperfect knowledge.

Also, FIG. 6 illustrates a graph for BLER versus SNR for a simulationfor a downlink solution with six active UEs and a 1 dB power offset.Cure 212 is for perfect CSI and perfect signature knowledge, curve 214is for perfect CSI with signature decorrelation using an MPA, curve 216is for channel estimation with perfect signature knowledge, and curve218 is for channel estimation with signature decorrelation and MPA. Forsix active UEs, LDS detection with signature decorrelation and an MPAworks comparably to LDS detection with perfect knowledge with a poweroffset of 1 dB.

FIG. 7 illustrates a graph of BLER versus SNR for a simulation with fouractive UEs and no power offset. Curve 222 is for perfect CSI and perfectsignature knowledge, curve 224 is for perfect CSI with signaturedecorrelation using an MPA, curve 226 is for channel estimation withperfect signature knowledge, and curve 228 is for channel estimationwith signature decorrelation using an MPA. With four active UEs and nopower offset, blind LDS detection with signature decorrelation performssimilarly to LDS detection with perfect knowledge.

FIG. 8 illustrates a graph of BLER versus SNR for a simulation with fouractive UEs and a power offset of 1 dB. Curve 232 is for perfect CSI andperfect signature knowledge, curve 234 is for perfect CSI with signaturedecorrelation using an MPA, curve 236 is for channel estimation andperfect signature knowledge, and curve 238 is for channel estimationwith signature decorrelation using an MPA. Blind LDS detection usingsignature decorrelation and MPA performs similarly to LDS detectionperfect signature knowledge with a power offset of 1 dB.

In another example, LDS is detected blindly using JMPA. In JMPA,detection of the data and the active signatures is performed jointly.FIG. 9 illustrates flowchart 140 showing a method of performing JMPA.Initially, in step 144, the signature pool is examined.

Then, in step 142, JMPA is performed based on the signature pool, andthe received signal. The data is decoded and the active signature listis determined.

Inactive signatures may be handled in multiple ways. To deal withinactive signatures, factor graph reduction may be used. FIG. 10illustrates factor graph 150, which contains active variable nodes 152,inactive variable nodes 154, and function nodes 156. The size of thefactor graph is reduced by removing inactive variable nodes 154 fromfactor graph 150. Alternatively, all variable nodes may exist, butinactive nodes have zero power.

FIG. 11 illustrates constellation 160. Assuming the originalconstellation size of variable nodes is QPSK, when a variable node isinactive, its corresponding value is zero. The effective constellationis expanded to four QPSK points plus point zero, for a constellationssize of five. Constellation point zero represents inactive signatures.An MPA may be initialized with a full factor graph assuming allsignatures are active. The WA then runs with a full factor graph and aconstellation size of five. At the end of an WA iteration, if a variablenode is detected with the highest probability of zero, the correspondingsignature is not determined to be active. When the highest probabilityis not zero at the end of an WA iteration, the log-likelihood ratios(LLRs) of the variable nodes are calculated based on the probability ofthe QPSK points.

JMPA may be treated as an WA where the number of constellation points isincreased by one. In JMPA, the probability of constellation point zerois the same for all LDS blocks. This provides for a natural coding gainwhich can be used to improve the performance of JMPA. The process forJMPA is similar to the process of MPA. In JMPA, the probability ofconstellation point zero is given by:

${p\left( {0,j} \right)}:={\frac{\underset{t}{\Pi}{p_{x}\left( {0,j,t} \right)}}{{\underset{t}{\Pi}\left( {1 - {p_{x}\left( {0,j,t} \right)}} \right)} + {\underset{t}{\Pi}{p_{x}\left( {0,j,t} \right)}}}.}$The probability of constellation point k from function node i tovariable node j at point t is given by:

p_(FN → VN)(i, j, k = 0, t) ← p(0, j) and$\left. {p_{{FN}\rightarrow{VN}}\left( {i,j,k,t} \right)}\leftarrow\frac{{p_{{FN}\rightarrow{VN}}\left( {i,j,k,t} \right)}\left( {1 - {p\left( {0,j} \right)}} \right)}{\sum\limits_{k = 1}^{4}{p_{{FN}\rightarrow{VN}}\left( {i,j,k,t} \right)}} \right.,{k = 1},\ldots\mspace{14mu},4.$

JMPA may be used with Turbo-MPA, as illustrated by flowchart 170 in FIG.12. Turbo-MPA is further discussed in provisional patent application No.61/788,881, filed on Mar. 15, 2013, which is hereby incorporated byreference. Flowchart 170 illustrates an outer-loop decision with a turbocode decoder and an outer-loop early determination detector. Step 172performs an MPA, as discussed above. The LLRs are output based on thereceived signal and a priori probabilities.

In step 174, soft input soft output (SISO) forward error correction(FEC) decoding is performed. SISO FEC may be performed separately foreach UE based on the input LLRs or probability values from the MPA.Output LLRs or probability values are also calculated.

In step 176, early termination of the MPA occurs based on the outer-looptermination indicator, e.g., when the updated probabilities convergeaccording to a threshold at FEC outputs. A priori information for LDSdetection is calculated based on the output of the SISO decoders. Thedifference between the LLRs is then calculated to obtain extrinsicinformation. Extrinsic information on the bits is used to update the apriori information about constellation points for every variable node orUE. An outer-loop convergence criterion may be defined to earlyterminate the outer loop iterations.

A hard decision is determined in step 182 based on the LLRs outputted inthe SISO FEC decoding. Hard decisions may take place when there is earlytermination. Alternatively, the hard decision may be performed after acertain number of iterations.

In a downlink scenario soft detection with JMPA may be used. However,the complexity may be high.

In an example, an uplink solution uses JMPA. In the case that the uplinkaccess is grant-less, the receiver does not know the identity of theuser or have channel or ordering knowledge. Also, in uplink, theactivation of different signatures is independent of each other. Hard orsoft detection with JMPA may be used as an uplink solution.

If the number of LDS blocks is sufficiently large, Γ_(i) can beapproximated by a Gaussian random variable with a mean of I_(i)γ_(i) anda variance of σ_(i) ², where Γ_(i) is the average received power foruser i. For hard detection,

$I_{i} = \left\{ {\begin{matrix}1 & {\Gamma_{i} \geq {th}} \\0 & {\Gamma_{i} < {th}_{i}}\end{matrix},{{{where}{th}_{i}} = {\frac{\gamma_{i}}{2}.}}} \right.$For soft detection,

${P_{ext}\left( {I_{i} = 1} \right)} = {\frac{{\exp\left( {- \frac{\left( {\Gamma_{i} - \gamma_{i}} \right)^{2}}{2\sigma_{i}^{2}}} \right)}{P_{ap}\left( {I_{i} = 1} \right)}}{{{\exp\left( {- \frac{\left( {\Gamma_{i} - \gamma_{i}} \right)^{2}}{2\sigma_{i}^{2}}} \right)}P_{ap}} = {\left( {I_{i} = 1} \right) + {{\exp\left( {- \frac{\Gamma_{i}^{2}}{2\sigma_{i}^{2}}} \right)}{P_{ap}\left( {I_{i} = 0} \right)}}}}.}$

FIG. 13 illustrates a block diagram of processing system 270 that may beused for implementing the devices and methods disclosed herein. Specificdevices may utilize all of the components shown, or only a subset of thecomponents, and levels of integration may vary from device to device.Furthermore, a device may contain multiple instances of a component,such as multiple processing units, processors, memories, transmitters,receivers, etc. The processing system may comprise a processing unitequipped with one or more input devices, such as a microphone, mouse,touchscreen, keypad, keyboard, and the like. Also, processing system 270may be equipped with one or more output devices, such as a speaker, aprinter, a display, and the like. The processing unit may includecentral processing unit (CPU) 274, memory 276, mass storage device 278,video adapter 280, and I/O interface 288 connected to a bus.

The bus may be one or more of any type of several bus architecturesincluding a memory bus or memory controller, a peripheral bus, videobus, or the like. CPU 274 may comprise any type of electronic dataprocessor. Memory 276 may comprise any type of system memory such asstatic random access memory (SRAM), dynamic random access memory (DRAM),synchronous DRAM (SDRAM), read-only memory (ROM), a combination thereof,or the like. In an embodiment, the memory may include ROM for use atboot-up, and DRAM for program and data storage for use while executingprograms.

Mass storage device 278 may comprise any type of storage deviceconfigured to store data, programs, and other information and to makethe data, programs, and other information accessible via the bus. Massstorage device 278 may comprise, for example, one or more of a solidstate drive, hard disk drive, a magnetic disk drive, an optical diskdrive, or the like.

Video adaptor 280 and I/O interface 288 provide interfaces to coupleexternal input and output devices to the processing unit. Asillustrated, examples of input and output devices include the displaycoupled to the video adapter and the mouse/keyboard/printer coupled tothe I/O interface. Other devices may be coupled to the processing unit,and additional or fewer interface cards may be utilized. For example, aserial interface card (not pictured) may be used to provide a serialinterface for a printer.

The processing unit also includes one or more network interface 284,which may comprise wired links, such as an Ethernet cable or the like,and/or wireless links to access nodes or different networks. Networkinterface 284 allows the processing unit to communicate with remoteunits via the networks. For example, the network interface may providewireless communication via one or more transmitters/transmit antennasand one or more receivers/receive antennas. In an embodiment, theprocessing unit is coupled to a local-area network or a wide-areanetwork for data processing and communications with remote devices, suchas other processing units, the Internet, remote storage facilities, orthe like.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

What is claimed is:
 1. A method for blindly detecting low densityactivity, the method comprising: receiving, by a first node from asecond node, a signal; and executing a joint message passing algorithm(JMPA) on the signal, wherein executing the JMPA includes jointlyproducing a decoded signal and an activity list in accordance with thedecoded signal, and calculating a plurality of a priori probabilities inaccordance with a plurality of log likelihood ratios (LLRs)corresponding to the signal and a plurality of decoded LLRs.
 2. Themethod of claim 1, wherein the first node is a user equipment (UE) andthe second node is a transmit point.
 3. The method of claim 1, whereinthe first node is a transmit point and the second node is a UE.
 4. Themethod of claim 1, wherein executing the JMPA further comprisesexecuting the JMPA in accordance with a factor graph.
 5. The method ofclaim 4, wherein an inactive signature is represented by a zeroconstellation point.
 6. The method of claim 1, wherein executing theJMPA further comprises: executing a message passage algorithm (MPA) toproduce the plurality of LLRs; decoding the plurality of LLRs to obtainthe plurality of decoded LLRs; and executing the MPA in accordance withthe signal and the plurality of a priori probabilities.
 7. The method ofclaim 1, wherein executing the JMPA further comprises performing harddetection on the signal.
 8. The method of claim 1, wherein executing theJMPA further comprises performing soft detection on the signal.
 9. Afirst node comprising: a processor; and a non-transitory computerreadable storage medium storing programming for execution by theprocessor, the programming including instructions to: receive a signalfrom a second node, and execute a joint message passing algorithm (JMPA)on the signal, to jointly produce an activity list in accordance with adecoded signal, and calculate a plurality of a priori probabilities inaccordance with a plurality of log likelihood ratios (LLRs)corresponding to the signal and a plurality of decoded LLRs.
 10. Thefirst node of claim 9, wherein the first node is a user equipment (UE)and the second node is a transmit point.
 11. The first node of claim 9,wherein the first node is a transmit point and the second node is a UE.12. The first node of claim 9, wherein the instructions to execute theJMPA further comprise instructions to execute the JMPA in accordancewith a factor graph.
 13. The first node of claim 12, wherein an inactivesignature is represented by a zero constellation point.
 14. The firstnode of claim 12, wherein the instructions to execute the JMPA furthercomprise instructions to: execute a message passage algorithm (MPA) toproduce the plurality of LLRs; decode the plurality of LLRs to obtainthe plurality of decoded LLRs; and execute the MPA in accordance withthe signal and the plurality of a priori probabilities.
 15. The firstnode of claim 12, wherein the instructions to execute the JMPA furthercomprise instructions to perform hard detection on the signal.
 16. Thefirst node of claim 12, wherein the instructions to execute the JMPAfurther comprise instructions to perform soft detection on the signal.17. A non-transitory computer readable storage medium storingprogramming for execution by a processor, the programming includinginstructions to: receive a signal from a second node; and execute ajoint message passing algorithm (JMPA) on the signal, to jointly producean activity list in accordance with a decoded signal, and calculate aplurality of a priori probabilities in accordance with a plurality oflog likelihood ratios (LLRs) corresponding to the signal and a pluralityof decoded LLRs.
 18. The storage medium of claim 17, wherein theinstructions to execute the JMPA further comprise instructions toexecute the JMPA in accordance with a factor graph.
 19. The storagemedium of claim 18, wherein an inactive signature is represented by azero constellation point.
 20. The storage medium of claim 17, whereinthe instructions to execute the JMPA further comprise instructions to:execute a message passage algorithm (MPA) to produce the plurality ofLLRs; decode the plurality of LLRs to obtain the plurality of decodedLLRs; and execute the MPA in accordance with the signal and theplurality of a priori probabilities.